Triple
T32902255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sixtus Beckmesser |
E841642
|
entity |
| Predicate | operaActCount |
P164243
|
FINISHED |
| Object | three-act opera character |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: three-act opera character | Statement: [Sixtus Beckmesser, operaActCount, three-act opera character]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operaActCount Context triple: [Sixtus Beckmesser, operaActCount, three-act opera character]
-
A.
numberOfOperas
Indicates the total count of operas associated with a given entity (such as a person, organization, or catalog entry).
-
B.
actCountInOpera
chosen
Indicates the number of acts that occur within a given opera.
-
C.
operaAct
Indicates that an entity performs in or takes part in an act (segment) of an opera performance.
-
D.
estimatedNumberOfOperas
Indicates the approximate count of operas associated with an entity, rather than an exact, verified number.
-
E.
operaNumberInComposerOutput
Indicates the ordinal position or catalog number assigned to an opera within the complete body of works by a specific composer.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f34946a5208190bbd79f0fec4323bd |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a003f6083608190a5deadc7291cf70e |
completed | May 10, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_6a003c935c40819085fdb255a52ba03b |
completed | May 10, 2026, 8:06 a.m. |
Created at: May 1, 2026, 1:19 a.m.